So I had this idea about how you could automatically analyze tons of NDE data…

Experience test-driving NDIToolbox in the field (or the depot / hangar to be more accurate) showed me that there is a ton of NDE sensor data out there and that it can take forever and a fortune to analyze manually. I’d experimented with algorithms to automatically flag possible indications of damage in the data when I was working on NDIToolbox and a project for the Air Force, but I’d never really gone beyond the proof of concept stage. Until recently I didn’t have a good handle on how to make it multi-processor and/or distributed, either – sitting in a depot for an hour waiting for a file to load has taught me single-threaded analysis isn’t feasible.

Two months or so in to the project and there’s a rather long demonstration of “Myriad” in action available, in which we train a machine learning model to automatically detect indications of damage in ultrasonic sensor data. I hope to have a few more demos of calling external apps or building a Myriad P2P cluster soon, stay tuned!